Kvasir Unet Segmentation

Input shape:

• 3 x 256 x 256

Output shape:

• 1 x 256 x 256

Loss function rationale

Medical image segmentation suffers from severe class imbalance because the foreground object occupies a small region of the image while the background dominates most pixels.

To address this, the model was trained with a combined loss:

• BCEWithLogitsLoss
• Dice Loss

Final loss:

• 0.5 * BCEWithLogitsLoss + 0.5 * DiceLoss

Preprocessing

• Resize to 256 x 256
• RGB normalization to [-1, 1]
• Masks resized with nearest-neighbor interpolation
• Masks binarized to {0, 1}

Training

• Hugging Face Trainer
• Epochs: 20
• Learning rate: 1e-3
• Batch size: 2

Results

• Test Dice: 0.7096
• Test IoU: 0.6066

--- language: en library_name: pytorch tags: - image-segmentation - medical-imaging - unet - kvasir datasets: - Angelou0516/kvasir-seg metrics: - dice - iou --- # Kvasir-SEG U-Net This repository contains a U-Net model trained for gastrointestinal polyp segmentation on the `Angelou0516/kvasir-seg` dataset. ## Architecture - U-Net implemented in PyTorch - Encoder with convolution + max-pooling - Bottleneck - Decoder with transposed convolutions - Skip connections - Final 1x1 convolution producing a single-channel mask Input shape: - `3 x 256 x 256` Output shape: - `1 x 256 x 256` ## Loss function rationale Medical image segmentation suffers from severe class imbalance because the foreground object occupies a small region of the image while the background dominates most pixels. To address this, the model was trained with a combined loss: - BCEWithLogitsLoss - Dice Loss Final loss: - `0.5 * BCEWithLogitsLoss + 0.5 * DiceLoss` ## Preprocessing - Resize to `256 x 256` - RGB normalization to `[-1, 1]` - Masks resized with nearest-neighbor interpolation - Masks binarized to `{0, 1}` ## Training - Hugging Face Trainer - Epochs: 20 - Learning rate: 1e-3 - Batch size: 2 ## Results - Test Dice: 0.7096 - Test IoU: 0.6066